An approach for structural static reanalysis with unchanged number of degrees of freedom

Authors:
H. F. Liu;B. S. Wu;C. W. Lim;Z. G. Li
Affiliations:
Department of Mathematics, School of Science, Xi'an Jiaotong University, Xi'an, People's Republic of China 710049;Department of Mechanics and Engineering Science, School of Mathematics, Jilin University, Changchun, People's Republic of China 130012;Department of Building and Construction, City University of Hong Kong, Kowloon, People's Republic of China;Department of Mechanics and Engineering Science, School of Mathematics, Jilin University, Changchun, People's Republic of China 130012
Venue:
Structural and Multidisciplinary Optimization
Year:
2012

Citing 6
Cited 0

GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems

SIAM Journal on Scientific and Statistical Computing
BI-CGSTAB: a fast and smoothly converging variant of BI-CG for the solution of nonsymmetric linear systems

SIAM Journal on Scientific and Statistical Computing
Matrix computations (3rd ed.)

Matrix computations (3rd ed.)
Preconditioning techniques for large linear systems: a survey

Journal of Computational Physics
Iterative Methods for Sparse Linear Systems

Iterative Methods for Sparse Linear Systems
A finite element algorithm for reanalysis of structures with added degrees of freedom

Finite Elements in Analysis and Design

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents an approach for structural static reanalysis with unchanged number of degrees of freedom. Preconditioned conjugate gradient method is employed, and a new preconditioner is constructed by updating the Cholesky factorization of the initial stiffness matrix with little cost. The proposed method preserves the ease of implementation and significantly improves the quality of the results. In particular, the accuracy of the approximate solutions can adaptively be monitored. Numerical examples show that the condition number of preconditioned system using the new preconditioner is much smaller than that using the initial stiffness matrix as the preconditioner. Therefore, the fast convergence and accurate results can be obtained by the proposed approach.